Priyank Jaini
- Artificial Intelligence
- Computer Networks and Communications top 10%
- Computer Vision and Pattern Recognition
- Cognitive Neuroscience
- Electrical and Electronic Engineering
- Topics
- Bayesian Methods and Mixture Models (3 papers)Generative Adversarial Networks and Image Synthesis (3 papers)Neural dynamics and brain function (2 papers)
- Cited by
- Computer Networks and CommunicationsArtificial IntelligenceComputer Vision and Pattern Recognition
- Partner nations
- CanadaUnited StatesChina
In The Last Decade
Priyank Jaini
14 papers receiving 155 citations
Peers
Comparison fields: 5 of 43
- Artificial Intelligence 71
- Computer Networks and Communications 63
- Computer Vision and Pattern Recognition 43
- Cognitive Neuroscience 37
- Electrical and Electronic Engineering 15
Countries citing papers authored by Priyank Jaini
This map shows the geographic impact of Priyank Jaini's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Priyank Jaini with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Priyank Jaini more than expected).
Fields of papers citing papers by Priyank Jaini
This network shows the impact of papers produced by Priyank Jaini. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Priyank Jaini. The network helps show where Priyank Jaini may publish in the future.
Co-authorship network of co-authors of Priyank Jaini
This figure shows the co-authorship network connecting the top 25 collaborators of Priyank Jaini. A scholar is included among the top collaborators of Priyank Jaini based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Priyank Jaini. Priyank Jaini is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | Argmax Flows and Multinomial Diffusion: Towards Non-Autoregressive Language Models. | 11 |
| 3 | Tails of Lipschitz Triangular Flows | 8 |
| 4 | SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows | 3 |
| 5 | 1 | |
| 6 | Argmax Flows: Learning Categorical Distributions with Normalizing Flows | 1 |
| 7 | 7 | |
| 8 | Tails of Triangular Flows. | 2 |
| 9 | Deep Homogeneous Mixture Models: Representation, Separation, and Approximation | 3 |
| 10 | Prometheus : Directly Learning Acyclic Directed Graph Structures for Sum-Product Networks | 2 |
| 11 | Online Bayesian Transfer Learning for Sequential Data Modeling | 11 |
| 12 | 20 | |
| 13 | 15 | |
| 14 | Online Algorithms for Sum-Product Networks with Continuous Variables | 6 |
| 15 | 68 |
About Priyank Jaini
Priyank Jaini is a scholar working on Artificial Intelligence, General Social Sciences and Signal Processing, having authored 15 papers that have together received 158 indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (3 papers), Generative Adversarial Networks and Image Synthesis (3 papers) and Neural dynamics and brain function (2 papers). The work is most often cited by research in Computer Networks and Communications (63 citations), Artificial Intelligence (71 citations) and Computer Vision and Pattern Recognition (43 citations). Priyank Jaini has collaborated with scholars based in Canada, United States and China. Frequent co-authors include Johannes Burge, Pascal Poupart, Zhitang Chen, Hao Jin, Hengky Susanto, Li Chen, Yanhui Geng, Kai Chen, Yaoliang Yu and Marcus A. Brubaker. Their work appears in journals such as PLoS Computational Biology, Journal of Vision and International Journal of Approximate Reasoning.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.